Synthetic Experiments. We use a Two-Stream Inflated 3D ConvNet (I3D) [1]} pretrained on the Kinetics Human Action Video Dataset [2]} as our baseline and feature extractor. The model is fine-tuned on our synthetic data. We use an Adam optimizer with an initial learning rate of \(1 \times 10^{-2}\)  and learning rate decay by a factor of 10 after every 10th epoch. We then use AMT to decouple synthetic bias cues from actions cues and show our results in Section REF .
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